Scale of Binary Variables for Predicting Cardiovascular Risk Scale for Predicting Cardiovascular Risk

Плехова, Н. Г.; Невзорова, В. А.; Родионова, Л. В.; Brodskaya, T. A.; Шуматов, В. Б.; Grunberg, K.V.; Dolzhikov, S. V.

doi:10.1109/rpc.2018.8482216

Cited by 5 publications

(2 citation statements)

References 10 publications

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“…Also it was shown that the transformation of regression model development cardiovascular diseases difficult for apply in practical medicine into a more simplified version of scale was shown. This included the determination of disease predictors; categorization and inclusion in the regression model with optimal scaling and coefficients for subsequent calculation of importance; plotting regression dependence of theoretical probability investigated variable; selection of threshold value [36,37].…”

Section: Telemedicine Diagnosismentioning

confidence: 99%

Scope and new horizons for implementation of m-Health/e-Health services in pulmonology in 2019

Mishlanov

Chuchalin²,

Chereshnev³

et al. 2019

Monaldi Arch Chest Dis

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The reason for this review based on the results of many meta- analyses is the great assessed difference in the methods of most studies in e-Health, telemedicine and tele-rehabilitation. It consists of different understanding of new terms, using different hard- and software, including criteria, different methodology of patient’s treatment and its evaluation. This status suggests that first of all m-Health/e-Health requires a unique ontology of terms using and methodology of studies comparing. In this review we try to describe shortly the most significant points of modern e-Health field of medicine. The basic parts include methodology of review formation, tele-communication implementation results, tele-education, interactive questioning, tele-consultation, telemedicine diagnosis, tele-monitoring, rehabilitation and tele-rehabilitation, gamification, acceptability of mobile electronic devices and software in e-Health and planning studies. At the end of the review the new ontological structure of digital medicine is presented.

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Section: Telemedicine Diagnosismentioning

confidence: 99%

Scope and new horizons for implementation of m-Health/e-Health services in pulmonology in 2019

Mishlanov

Chuchalin²,

Chereshnev³

et al. 2019

Monaldi Arch Chest Dis

View full text Add to dashboard Cite

show abstract

“…Известно небольшое количество исследований, где были изучены потенциальные преимущества использования подходов МО для прогнозирования риска ССЗ. Продемонстрировано, что, по сравнению с приведенными выше шкалами оценки, МО значительно повышает точность прогнозирования риска ССЗ, увеличивая количество пациентов, которые могли бы получить пользу в большей степени от профилактического лечения до проявления клинически значимых признаков [2][3][4]. В настоящей работе приведена потенциальная ценность использования подходов МО для построения модели прогнозирования риска ССЗ с учетом показателей артериального давления (АД).…”

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Machine learning for predicting the outcomes and risks of cardiovascular diseases in patients with hypertension: results of ESSE-RF in the Primorsky Krai

et al. 2020

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Aim. To assess the prospects of using artificial intelligence technologies in predicting the outcomes and risks of cardiovascular diseases (CVD) in patients with hypertension (HTN).Material and methods. A software application was created for data mining from respondent profiles in a semi-automatic mode; libraries with data preprocessing were analyzed. We analyzed the main and additional parameters (35) of CVD risk factors in 2131 people as a part of ESSE-RF study (2014-2019). To create a forecasting model, a high-level language Python 2.7 was used using object-oriented programming and exception handling with multithreading support. Using randomization, learning (n=488) and test (n=245) samples were formed, which included data from patients with an established diagnosis of HTN.Results. The prevalence of HTN among subjects was 34,39%. There were following significant factors for predicting CVD: anthropometric parameters, smoking, biochemical profile (total cholesterol, ApoA, ApoB, glucose, D-dimer, C-reactive protein). As a result of a 5-year follow-up, CVD was found in 235 people (32,06%) with HTN and 187 people (13,38%) without HTN; mortality rates were 1,27% in subjects with HTN and 1,12% — without HTN. The absolute mortality risk among participants with HTN (0,037) was significantly higher (p<0,05) than in patients without HTN (0,017). To create a neural network (NN), the basic Sequential model from the Keras library was used. During machine learning, 26 variables important for the CVD development were used as input and 9 neurons — as output, which corresponded to the number of established cardiovascular events. The created NN had a predictive value of up to 97,9%, which exceeded the SCORE value (34,9%).Conclusion. The data obtained indicate the importance of risk factor phenotyping using anthropometric markers and biochemical profile for determining their significance in the top 20 predictors of CVD. The Python-based machine learning provides CVD prediction according to standard risk assessments.

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Predicting Cardiovascular Risk Level Based on Biochemical Risk Factor Indicators Using Machine Learning: A Case Study in Indonesia

Heryadi

Kosala

Bahana

et al. 2019

Intelligent Information and Database Systems

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Scale of Binary Variables for Predicting Cardiovascular Risk Scale for Predicting Cardiovascular Risk

Cited by 5 publications

References 10 publications

Scope and new horizons for implementation of m-Health/e-Health services in pulmonology in 2019

Scope and new horizons for implementation of m-Health/e-Health services in pulmonology in 2019

Machine learning for predicting the outcomes and risks of cardiovascular diseases in patients with hypertension: results of ESSE-RF in the Primorsky Krai

Predicting Cardiovascular Risk Level Based on Biochemical Risk Factor Indicators Using Machine Learning: A Case Study in Indonesia

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